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Packt Publishing

Python for Finance Cookbook: Over 80 powerful recipes for effective financial data analysis, 2nd Edition

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Use modern Python libraries such as pandas, NumPy, and scikit-learn and popular machine learning and deep learning methods to solve financial modeling problemsPurchase of the print or Kindle book includes a free eBook in the PDF formatKey FeaturesExplore unique recipes for financial data processing and analysis with PythonApply classical and machine learning approaches to financial time series analysisCalculate various technical analysis indicators and backtest trading strategiesBook DescriptionPython is one of the most popular programming languages in the financial industry, with a huge collection of accompanying libraries. In this new edition of the Python for Finance Cookbook, you will explore classical quantitative finance approaches to data modeling, such as GARCH, CAPM, factor models, as well as modern machine learning and deep learning solutions.You will use popular Python libraries that, in a few lines of code, provide the means to quickly process, analyze, and draw conclusions from financial data. In this new edition, more emphasis was put on exploratory data analysis to help you visualize and better understand financial data. While doing so, you will also learn how to use Streamlit to create elegant, interactive web applications to present the results of technical analyses.Using the recipes in this book, you will become proficient in financial data analysis, be it for personal or professional projects. You will also understand which potential issues to expect with such analyses and, more importantly, how to overcome them.What you will learnPreprocess, analyze, and visualize financial dataExplore time series modeling with statistical (exponential smoothing, ARIMA) and machine learning modelsUncover advanced time series forecasting algorithms such as Meta's ProphetUse Monte Carlo simulations for derivatives valuation and risk assessmentExplore volatility modeling using univariate and multivariate GARCH modelsInvestigate various approaches to asset allocationLearn how to approach ML-projects using an example of default predictionExplore modern deep learning models such as Google's TabNet, Amazon's DeepAR and NeuralProphetWho this book is forThis book is intended for financial analysts, data analysts and scientists, and Python developers with a familiarity with financial concepts. You'll learn how to correctly use advanced approaches for analysis, avoid potential pitfalls and common mistakes, and reach correct conclusions for a broad range of finance problems.Working knowledge of the Python programming language (particularly libraries such as pandas and NumPy) is necessary.Table of ContentsAcquiring Financial DataData PreprocessingVisualizing Financial Time SeriesExploring Financial Time Series DataTechnical Analysis and Building Interactive DashboardsTime Series Analysis and ForecastingMachine Learning- Based Approaches to Time Series ForecastingMulti-Factor ModelsModelling Volatility with GARCH Class ModelsMonte Carlo Simulations in FinanceAsset AllocationBacktesting Trading StrategiesApplied Machine Learning: Identifying Credit DefaultAdvanced Concepts for Machine Learning ProjectsDeep Learning in Finance Read more

Publisher ‏ : ‎ Packt Publishing


Publication date ‏ : ‎ December 30, 2022


Edition ‏ : ‎ 2nd ed.


Language ‏ : ‎ English


Print length ‏ : ‎ 740 pages


ISBN-10 ‏ : ‎ 1803243198


ISBN-13 ‏ : ‎ 91


Item Weight ‏ : ‎ 2.75 pounds


Dimensions ‏ : ‎ 7.5 x 1.67 x 9.25 inches


Best Sellers Rank: #52,976 in Books (See Top 100 in Books) #6 in Business Finance #7 in Data Modeling & Design (Books) #29 in Python Programming


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Top Amazon Reviews

    • Excellent book. Must have as a reference
Excellent book. Good discussion on each topic, self-contained code examples and they work! I hope author will come up with an updated version covering newer techniques in detail e.g., using pytorch !! Must have as a reference
Reviewed in the United States on April 24, 2025 by Par
    • Book helped me to find a 0.8 sharpe ratio algo
Review Image by Li Siu Chun Li Siu Chun
Review Image by Li Siu Chun Li Siu Chun
Time series section gave me info and idea to find out a sharpe ratio 0.8 strategies (2020-). But i rate this book as 4 stars as the model factor section makes no sense to me. I am expecting the author shows me cookbook to use stock leading factor (as mentioned in the opening of the section in the book) to put in the model and how to use this model on algo strategy. But it turns out the code example is nothing related to it. ... show more
Reviewed in the United States on October 19, 2024 by Li Siu Chun Li Siu Chun
    • Great Book
Really great book with super detailed explanations. I was honestly amazed at how clearly and systematically everything was explained — it made it so much easier to follow and stay interested.
Reviewed in the United States on April 29, 2025 by HVB
    • Good book
Love this book since it is really useful for my study and work!
Reviewed in the United States on May 6, 2025 by Suong Tran
    • An engaging and comprehensive guide to mastering financial data analysis with Python
The Python for Finance Cookbook offers readers a comprehensive introduction to the world of financial data analysis using Python. In addition to being well-structured, the book guides readers through logical progressions of topics from the very beginning. Throughout the book, each chapter is organized in such a manner that it is easy to follow along and comprehend what is being discussed. This book is incredibly helpful in that it breaks down scenarios into sections, beginning with "How to do it," followed by "How it works," and concluding with "There's more.". As a result of this format, readers can gain an understanding of Python techniques, their practical implementation, and additional options. Coding examples provided are clear and concise, making application to real-world financial problems easy. The author's explanations held my attention throughout the reading of the book. Each example presented a well-articulated thought process that guided me through the steps of the analysis and decision-making process. As a result of the author's clear language and the inclusion of practical tips, I was able to avoid common mistakes and gain a greater understanding of financial data analysis. Time series analysis, technical analysis, machine learning, and deep learning are all discussed in the book. In addition, Streamlit is introduced, which is a valuable tool for developing interactive web applications for presenting analysis results. An important aspect of the course was the emphasis on exploratory data analysis, which allowed me to uncover insights and draw meaningful conclusions from the financial information. As a whole, I found the "Python for Finance Cookbook" to be an extremely helpful resource. In this book, you will learn how to analyze financial data using Python to enhance your skills as a financial analyst, data scientist, or Python developer. My proficiency in using Python for financial data analysis has significantly improved as a result of its well-structured outline, detailed explanations, and thought-provoking examples provided in this book. To anyone seeking a deeper understanding of this field, I strongly recommend this book. ... show more
Reviewed in the United States on May 29, 2023 by Gowtham Varma Bhupathiraju
    • Enjoying the book
As the title states is a Cookbook. It introduces many python libraries to analyzed and apply Machine Learning (ML) applications to financial time series data. Does a great job on how to download financial data. All the code in the book is available from the URLs provided in the book. I found the book very useful and recommend the book, for those getting started in analyzing and forecasting financial time series data in python. ... show more
Reviewed in the United States on March 29, 2024 by Carlos P.
    • Almost worthless book
This is one of the worst books I have ever bought. I like to copy over the code and run it myself. Time after time, the code would not work, even after using GITHUB for a most current version.
Reviewed in the United States on March 19, 2025 by Michael L Meriwether
    • Great for Data Analyst's with an interest in finance and investing!
I recently picked up "Python for Finance Cookbook," which is tailored perfectly for a data analyst like me who studies finance and investing on the side, and it is an understatement to say I was truly impressed! This book seamlessly integrates the complexities of finance with the versatility of Python, offering an invaluable guide to harnessing financial data for insightful analysis. Right from the start, Chapter 1, "Acquiring Financial Data," grabbed my attention. As someone who values accurate data, the step-by-step instructions for gathering data from diverse sources like Yahoo Finance, Nasdaq Data Link, and more were indispensable. Chapter 2's data preprocessing techniques, covering everything from handling missing data to adjusting for inflation, were equally beneficial, streamlining my analysis process. The book's coverage of visualizing financial time series data in Chapter 3 elevated my understanding of plotting financial data. Techniques like creating interactive visualizations and understanding seasonal patterns provided fresh perspectives on market behavior. Chapters 4 to 6 further explored data analysis and forecasting, while Chapters 7 and 8 bridged the gap between data analysis and investment strategy, showing how machine learning can enhance forecasting and estimating models. As I delved deeper, the book's advanced topics, including volatility modeling, Monte Carlo simulations, and deep learning applications, kept pushing my boundaries. Each chapter concluded with a concise summary, reinforcing key takeaways and ensuring I grasped the essentials. In summary, the "Python for Finance Cookbook" is a treasure trove for data analysts with an investing interest. It fuses Python programming with financial concepts seamlessly, empowering readers to confidently analyze data, forecast trends, and make informed investment choices. My skills as a data analyst and investor have undeniably grown through this book, and I'm excited to implement its insights in my future pursuits. ... show more
Reviewed in the United States on August 10, 2023 by Richard

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